US 12,243,070 B2
Contextual recommendations with graphical elements based on activity recognition
Alex Heath Misiaszek, Cornelius, NC (US); and Sarah Katherine Nash, Raleigh, NC (US)
Assigned to Truist Bank, Charlotte, NC (US)
Filed by Truist Bank, Charlotte, NC (US)
Filed on Aug. 25, 2022, as Appl. No. 17/895,626.
Prior Publication US 2024/0070710 A1, Feb. 29, 2024
Int. Cl. G06Q 30/02 (2023.01); G06N 20/00 (2019.01); G06Q 30/0207 (2023.01); G06Q 30/0234 (2023.01)
CPC G06Q 30/0234 (2013.01) [G06N 20/00 (2019.01); G06Q 30/0239 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of automatically populating graphical elements on a user interface of a computer system comprising:
receiving, by the computer system, sets of information from a plurality of third-party server operators, the sets of information comprising one or more services provided by the plurality of third-party server operators;
creating, for each set of information, a graphical element for the user interface, the graphical element indicating at least one service provided in a respective set of information received from the plurality of third-party operators;
collecting, via the user interface, a first set of data from a plurality of users;
training a machine-learning model to determine at least one pattern in activities using the first set of data;
collecting, via the user interface, a second set of data from a specific user;
applying the machine-learning model to the second set of data to predict a pattern in activities associated with the specific user, wherein the predicted pattern comprises an evolving list of graphical element categories to associate with a marital, parental, or career status of the specific user;
generating, by the machine-learning model, a ranking of a set of potential graphical elements based on the predicted pattern; and
automatically populating at least two graphical elements from the set of potential graphical elements for the user interface based on the pattern in activities associated with the specific user and arranging the at least two graphical elements in order from a top towards a bottom of the user interface based on the ranking generated by the machine-learning model.